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Zhang D, Martin RV, van Donkelaar A, Li C, Zhu H, Lyapustin A. Impact of Model Spatial Resolution on Global Geophysical Satellite-Derived Fine Particulate Matter. ACS ES&T AIR 2024; 1:1112-1123. [PMID: 39295744 PMCID: PMC11407304 DOI: 10.1021/acsestair.4c00084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/09/2024] [Revised: 07/15/2024] [Accepted: 07/16/2024] [Indexed: 09/21/2024]
Abstract
Global geophysical satellite-derived ambient fine particulate matter (PM2.5) inference relies upon a geophysical relationship (η) from a chemical transport model to relate satellite retrievals of aerosol optical depth (AOD) to surface PM2.5. The resolution dependence of simulated η warrants further investigation. In this study, we calculate geophysical PM2.5 with simulated η from the GEOS-Chem model in its high-performance configuration (GCHP) at cubed-sphere resolutions of C360 (∼25 km) and C48 (∼200 km) and satellite AOD at 0.01° (∼1 km). Annual geophysical PM2.5 concentrations inferred from satellite AOD and GCHP simulations at ∼25 km and ∼200 km resolutions exhibit remarkable similarity (R 2 = 0.96, slope = 1.03). This similarity in part reflects opposite resolution responses across components with population-weighted normalized mean difference (PW-NMD) increasing by 5% to 11% for primary species while decreasing by -30% to -5% for secondary species at finer resolution. Despite global similarity, our results also identify larger resolution sensitivities of η over isolated pollution sources and mountainous regions, where spatial contrast of aerosol concentration and composition is better represented at fine resolution. Our results highlight the resolution dependence of representing near-surface concentrations and the vertical distribution of chemically different species with implications for inferring ground-level PM2.5 from columnar AOD.
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Affiliation(s)
- Dandan Zhang
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, Missouri 63130, United States
| | - Randall V Martin
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, Missouri 63130, United States
| | - Aaron van Donkelaar
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, Missouri 63130, United States
| | - Chi Li
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, Missouri 63130, United States
| | - Haihui Zhu
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, Missouri 63130, United States
| | - Alexei Lyapustin
- Climate and Radiation Laboratory, the National Aeronautics and Space Administration Goddard Space Flight Center, Greenbelt, Maryland 20771, United States
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2
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Ye X, Zhang L, Wang X, Lu X, Jiang Z, Lu N, Li D, Xu J. Spatial and temporal variations of surface background ozone in China analyzed with the grid-stretching capability of GEOS-Chem High Performance. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 914:169909. [PMID: 38185162 DOI: 10.1016/j.scitotenv.2024.169909] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 12/11/2023] [Accepted: 01/02/2024] [Indexed: 01/09/2024]
Abstract
Surface background ozone, defined as the ozone in the absence of domestic anthropogenic emissions, is important for developing emission reduction strategies. Here we apply the recently developed GEOS-Chem High Performance (GCHP) global atmospheric chemistry model with ∼0.5° stretched resolution over China to understand the sources of Chinese background ozone (CNB) in the metric of daily maximum 8 h average (MDA8) and to identify the drivers of its interannual variability (IAV) from 2015 to 2019. The GCHP ozone simulations over China are evaluated with an ensemble of surface and aircraft measurements. The five-year national-mean CNB ozone is estimated as 37.9 ppbv, with a spatially west-to-southeast downward gradient (55 to 25 ppbv) and a summer peak (42.5 ppbv). High background levels in western China are due to abundant transport from the free troposphere and adjacent foreign regions, while in eastern China, domestic formation from surface natural precursors is also important. We find greater importance of soil nitric oxides (NOx) than biogenic volatile organic compound emissions to CNB ozone in summer (6.4 vs. 3.9 ppbv), as ozone formation becomes increasingly NOx-sensitive when suppressing anthropogenic emissions. The percentage of daily CNB ozone to total surface ozone generally decreases with increasing daily total ozone, indicating an increased contribution of domestic anthropogenic emissions on polluted days. CNB ozone shows the largest IAV in summer, with standard deviations (seasonal means) of ∼5 ppbv over Qinghai-Tibet Plateau (QTP) and >3.5 ppbv in eastern China. CNB values in QTP are strongly correlated with horizontal circulation anomalies in the middle troposphere, while soil NOx emissions largely drive the IAV in the east. El Nino can inhibit CNB ozone formation in Southeast China by increased precipitation and lower temperature locally in spring, but enhance CNB in Southwest China through increased biomass burning emissions in Southeast Asia.
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Affiliation(s)
- Xingpei Ye
- Laboratory for Climate and Ocean-Atmosphere Studies, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing 100871, China
| | - Lin Zhang
- Laboratory for Climate and Ocean-Atmosphere Studies, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing 100871, China.
| | - Xiaolin Wang
- Laboratory for Climate and Ocean-Atmosphere Studies, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing 100871, China
| | - Xiao Lu
- School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai, Guangdong, China
| | - Zhongjing Jiang
- Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, NY 11973-5000, United States of America
| | - Ni Lu
- Laboratory for Climate and Ocean-Atmosphere Studies, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing 100871, China
| | - Danyang Li
- Laboratory for Climate and Ocean-Atmosphere Studies, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing 100871, China
| | - Jiayu Xu
- Laboratory for Climate and Ocean-Atmosphere Studies, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing 100871, China
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3
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Liu Z, Zeng N, Liu Y, Wang J, Han P, Cai Q. Weaker regional carbon uptake albeit with stronger seasonal amplitude in northern mid-latitudes estimated by higher resolution GEOS-Chem model. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 912:169477. [PMID: 38143002 DOI: 10.1016/j.scitotenv.2023.169477] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 11/27/2023] [Accepted: 12/16/2023] [Indexed: 12/26/2023]
Abstract
Terrestrial ecosystem in the Northern Hemisphere is characterized by a substantial carbon sink in recent decades. However, the carbon sink inferred from atmospheric CO2 data is usually larger than process- and inventory-based estimates, resulting in carbon release or near-neutral carbon exchange in the tropics. The atmospheric approach is known to be uncertain due to systematic biases of coarse atmospheric transport model simulation. Compared to a coarse-resolution inverse estimate at 4° × 5° using GEOS-Chem in the integrated region of N. America, E. Asia, and Europe from 2015 to 2018, the annual carbon sink estimate at a native high-resolution of 0.5° × 0.625° is reduced from -3.0±0.08 gigatons of carbon per year (GtC yr-1) to -2.15±0.08 GtC yr-1 due to prominent more carbon release during the non-growing seasons. The major reductions concentrate in the mid-latitudes (20°N-45°N), where the mean land carbon sinks in China and the USA are reduced from 0.64±0.03 and 0.35±0.02 GtC yr-1 to 0.14±0.03 and 0.15±0.02 GtC yr-1, respectively. The coarse-resolution GEOS-Chem tends to trap both the release and uptake signal within the planetary boundary layer, resulting in weaker estimates of biosphere seasonal strength. Since the strong fossil fuel emissions are persistently released from the surface, the trapped signal leads to the stronger estimates of annual carbon uptakes. These results suggest that high-resolution inversion with accurate vertical and meridional transport is urgently needed in targeting national carbon neutrality.
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Affiliation(s)
- Zhiqiang Liu
- CMA Key Open Laboratory of Transforming Climate Resources to Economy, Chongqing Institute of Meteorological Sciences, Chongqing 401147, China.
| | - Ning Zeng
- Dept. of Atmospheric and Oceanic Science, University of Maryland, College Park, Maryland, USA; Earth System Science Interdisciplinary Center, University of Maryland, College Park, Maryland, USA.
| | - Yun Liu
- Geochemical and Environment Research Group, Texas A&M University, College Station, TX, USA
| | - Jun Wang
- International Institute for Earth System Science, Nanjing University, Nanjing, China
| | - Pengfei Han
- Carbon Neutrality Research Center, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China; Laboratory of Numerical Modeling for Atmospheric Sciences & Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
| | - Qixiang Cai
- Laboratory of Numerical Modeling for Atmospheric Sciences & Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
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4
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Zhang D, Martin RV, Bindle L, Li C, Eastham SD, van Donkelaar A, Gallardo L. Advances in Simulating the Global Spatial Heterogeneity of Air Quality and Source Sector Contributions: Insights into the Global South. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:6955-6964. [PMID: 37079489 PMCID: PMC10158787 DOI: 10.1021/acs.est.2c07253] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Revised: 04/05/2023] [Accepted: 04/06/2023] [Indexed: 05/03/2023]
Abstract
High-resolution simulations are essential to resolve fine-scale air pollution patterns due to localized emissions, nonlinear chemical feedbacks, and complex meteorology. However, high-resolution global simulations of air quality remain rare, especially of the Global South. Here, we exploit recent developments to the GEOS-Chem model in its high-performance implementation to conduct 1-year simulations in 2015 at cubed-sphere C360 (∼25 km) and C48 (∼200 km) resolutions. We investigate the resolution dependence of population exposure and sectoral contributions to surface fine particulate matter (PM2.5) and nitrogen dioxide (NO2), focusing on understudied regions. Our results indicate pronounced spatial heterogeneity at high resolution (C360) with large global population-weighted normalized root-mean-square difference (PW-NRMSD) across resolutions for primary (62-126%) and secondary (26-35%) PM2.5 species. Developing regions are more sensitive to spatial resolution resulting from sparse pollution hotspots, with PW-NRMSD for PM2.5 in the Global South (33%), 1.3 times higher than globally. The PW-NRMSD for PM2.5 for discrete southern cities (49%) is substantially higher than for more clustered northern cities (28%). We find that the relative order of sectoral contributions to population exposure depends on simulation resolution, with implications for location-specific air pollution control strategies.
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Affiliation(s)
- Dandan Zhang
- Department
of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, Missouri 63130, United States
| | - Randall V. Martin
- Department
of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, Missouri 63130, United States
| | - Liam Bindle
- Department
of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, Missouri 63130, United States
| | - Chi Li
- Department
of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, Missouri 63130, United States
| | - Sebastian D. Eastham
- Laboratory
for Aviation and the Environment, Massachusetts
Institute of Technology, Cambridge, Massachusetts 02139, United States
- Joint
Program on the Science and Policy of Global Change, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Aaron van Donkelaar
- Department
of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, Missouri 63130, United States
| | - Laura Gallardo
- Center
for Climate and Resilience Research, Santiago 8370448, Chile
- Department
of Geophysics, Faculty of Physical Sciences and Mathematics, University of Chile, Santiago 8370448, Chile
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5
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Ye X, Wang X, Zhang L. Diagnosing the Model Bias in Simulating Daily Surface Ozone Variability Using a Machine Learning Method: The Effects of Dry Deposition and Cloud Optical Depth. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2022; 56:16665-16675. [PMID: 36437714 DOI: 10.1021/acs.est.2c05712] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Machine learning methods are increasingly used in air quality studies to predict air pollution levels, while few applied them to diagnose and improve the underlying mechanisms controlling air pollution represented in chemical transport models (CTMs). Here, we use the random forest (RF) method to diagnose high biases of surface daily maximum 8 h average (MDA8) ozone concentrations in the GEOS-Chem CTM evaluated against measurements from the nationwide monitoring network in summer 2018 over China. The feature importance results show that cloud optical depth (COD), relative humidity, and precipitation are the top three factors affecting CTM high biases. Such results indicate that the high ozone biases in summer over China mainly occur on wet/cloudy days (∼40% biased high), while biases on dry/clear days are small (within 5%). We link the important features with model parameterizations and variables, identifying model underestimates in the dry deposition velocity and COD on wet/cloudy days. By accounting for the enhanced dry deposition on wet plant cuticles and using satellite observation constrained COD, we find that CTM high ozone biases can be halved with an improved agreement in the temporal variability, highlighting the effects of dry deposition and COD on ozone, as suggested by the RF outcomes.
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Affiliation(s)
- Xingpei Ye
- Laboratory for Climate and Ocean-Atmosphere Studies, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing100871, China
| | - Xiaolin Wang
- Laboratory for Climate and Ocean-Atmosphere Studies, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing100871, China
| | - Lin Zhang
- Laboratory for Climate and Ocean-Atmosphere Studies, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing100871, China
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6
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Moch JM, Mickley LJ, Keller CA, Bian H, Lundgren EW, Zhai S, Jacob DJ. Aerosol-Radiation Interactions in China in Winter: Competing Effects of Reduced Shortwave Radiation and Cloud-Snowfall-Albedo Feedbacks Under Rapidly Changing Emissions. JOURNAL OF GEOPHYSICAL RESEARCH. ATMOSPHERES : JGR 2022; 127:e2021JD035442. [PMID: 35859567 PMCID: PMC9285729 DOI: 10.1029/2021jd035442] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Revised: 03/08/2022] [Accepted: 04/17/2022] [Indexed: 06/15/2023]
Abstract
Since 2013, Chinese policies have dramatically reduced emissions of particulates and their gas-phase precursors, but the implications of these reductions for aerosol-radiation interactions are unknown. Using a global, coupled chemistry-climate model, we examine how the radiative impacts of Chinese air pollution in the winter months of 2012 and 2013 affect local meteorology and how these changes may, in turn, influence surface concentrations of PM2.5, particulate matter with diameter <2.5 μm. We then investigate how decreasing emissions through 2016 and 2017 alter this impact. We find that absorbing aerosols aloft in winter 2012 and 2013 heat the middle- and lower troposphere by ∼0.5-1 K, reducing cloud liquid water, snowfall, and snow cover. The subsequent decline in surface albedo appears to counteract the ∼15-20 W m-2 decrease in shortwave radiation reaching the surface due to attenuation by aerosols overhead. The net result of this novel cloud-snowfall-albedo feedback in winters 2012-2013 is a slight increase in surface temperature of ∼0.5-1 K in some regions and little change elsewhere. The aerosol heating aloft, however, stabilizes the atmosphere and decreases the seasonal mean planetary boundary layer (PBL) height by ∼50 m. In winter 2016 and 2017, the ∼20% decrease in mean PM2.5 weakens the cloud-snowfall-albedo feedback, though it is still evident in western China, where the feedback again warms the surface by ∼0.5-1 K. Regardless of emissions, we find that aerosol-radiation interactions enhance mean surface PM2.5 pollution by 10%-20% across much of China during all four winters examined, mainly though suppression of PBL heights.
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Affiliation(s)
- Jonathan M. Moch
- John A. Paulson School of Engineering and Applied SciencesHarvard UniversityCambridgeMAUSA
- Department of Earth and Planetary SciencesHarvard UniversityCambridgeMAUSA
| | - Loretta J. Mickley
- John A. Paulson School of Engineering and Applied SciencesHarvard UniversityCambridgeMAUSA
| | - Christoph A. Keller
- Global Modeling and Assimilation OfficeNASA Goddard Space Flight CenterGreenbeltMDUSA
- Universities Space Research AssociationColumbiaMDUSA
| | - Huisheng Bian
- Global Modeling and Assimilation OfficeNASA Goddard Space Flight CenterGreenbeltMDUSA
- Universities Space Research AssociationColumbiaMDUSA
| | - Elizabeth W. Lundgren
- John A. Paulson School of Engineering and Applied SciencesHarvard UniversityCambridgeMAUSA
| | - Shixian Zhai
- John A. Paulson School of Engineering and Applied SciencesHarvard UniversityCambridgeMAUSA
| | - Daniel J. Jacob
- John A. Paulson School of Engineering and Applied SciencesHarvard UniversityCambridgeMAUSA
- Department of Earth and Planetary SciencesHarvard UniversityCambridgeMAUSA
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7
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Assimilation of GOSAT Methane in the Hemispheric CMAQ; Part II: Results Using Optimal Error Statistics. REMOTE SENSING 2022. [DOI: 10.3390/rs14020375] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
We applied the parametric variance Kalman filter (PvKF) data assimilation designed in Part I of this two-part paper to GOSAT methane observations with the hemispheric version of CMAQ to obtain the methane field (i.e., optimized analysis) with its error variance. Although the Kalman filter computes error covariances, the optimality depends on how these covariances reflect the true error statistics. To achieve more accurate representation, we optimize the global variance parameters, including correlation length scales and observation errors, based on a cross-validation cost function. The model and the initial error are then estimated according to the normalized variance matching diagnostic, also to maintain a stable analysis error variance over time. The assimilation results in April 2010 are validated against independent surface and aircraft observations. The statistics of the comparison of the model and analysis show a meaningful improvement against all four types of available observations. Having the advantage of continuous assimilation, we showed that the analysis also aims at pursuing the temporal variation of independent measurements, as opposed to the model. Finally, the performance of the PvKF assimilation in capturing the spatial structure of bias and uncertainty reduction across the Northern Hemisphere is examined, indicating the capability of analysis in addressing those biases originated, whether from inaccurate emissions or modelling error.
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8
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Dadashazar H, Alipanah M, Hilario MRA, Crosbie E, Kirschler S, Liu H, Moore RH, Peters AJ, Scarino AJ, Shook M, Thornhill KL, Voigt C, Wang H, Winstead E, Zhang B, Ziemba L, Sorooshian A. Aerosol responses to precipitation along North American air trajectories arriving at Bermuda. ATMOSPHERIC CHEMISTRY AND PHYSICS 2021; 21:16121-16141. [PMID: 34819950 PMCID: PMC8609468 DOI: 10.5194/acp-21-16121-2021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
North American pollution outflow is ubiquitous over the western North Atlantic Ocean, especially in winter, making this location a suitable natural laboratory for investigating the impact of precipitation on aerosol particles along air mass trajectories. We take advantage of observational data collected at Bermuda to seasonally assess the sensitivity of aerosol mass concentrations and volume size distributions to accumulated precipitation along trajectories (APT). The mass concentration of particulate matter with aerodynamic diameter less than 2.5 μm normalized by the enhancement of carbon monoxide above background (PM2.5/ΔCO) at Bermuda was used to estimate the degree of aerosol loss during transport to Bermuda. Results for December-February (DJF) show that most trajectories come from North America and have the highest APTs, resulting in a significant reduction (by 53 %) in PM2.5/ΔCO under high-APT conditions (> 13.5 mm) relative to low-APT conditions (< 0.9 mm). Moreover, PM2.5/ΔCO was most sensitive to increases in APT up to 5 mm (-0.044 μg m-3 ppbv-1 mm-1) and less sensitive to increases in APT over 5 mm. While anthropogenic PM2.5 constituents (e.g., black carbon, sulfate, organic carbon) decrease with high APT, sea salt, in contrast, was comparable between high- and low-APT conditions owing to enhanced local wind and sea salt emissions in high-APT conditions. The greater sensitivity of the fine-mode volume concentrations (versus coarse mode) to wet scavenging is evident from AErosol RObotic NETwork (AERONET) volume size distribution data. A combination of GEOS-Chem model simulations of the 210Pb submicron aerosol tracer and its gaseous precursor 222Rn reveals that (i) surface aerosol particles at Bermuda are most impacted by wet scavenging in winter and spring (due to large-scale precipitation) with a maximum in March, whereas convective scavenging plays a substantial role in summer; and (ii) North American 222Rn tracer emissions contribute most to surface 210Pb concentrations at Bermuda in winter (~75 %-80 %), indicating that air masses arriving at Bermuda experience large-scale precipitation scavenging while traveling from North America. A case study flight from the ACTIVATE field campaign on 22 February 2020 reveals a significant reduction in aerosol number and volume concentrations during air mass transport off the US East Coast associated with increased cloud fraction and precipitation. These results highlight the sensitivity of remote marine boundary layer aerosol characteristics to precipitation along trajectories, especially when the air mass source is continental outflow from polluted regions like the US East Coast.
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Affiliation(s)
- Hossein Dadashazar
- Department of Chemical and Environmental Engineering, University of Arizona, Tucson, AZ, USA
| | - Majid Alipanah
- Department of Systems and Industrial Engineering, University of Arizona, Tucson, AZ, USA
| | | | - Ewan Crosbie
- NASA Langley Research Center, Hampton, VA, USA
- Science Systems and Applications, Inc., Hampton, VA, USA
| | - Simon Kirschler
- Institute for Atmospheric Physics, DLR, German Aerospace Center, Oberpfaffenhofen, Germany
- Institute for Atmospheric Physics, University of Mainz, Mainz, Germany
| | - Hongyu Liu
- National Institute of Aerospace, Hampton, VA, USA
| | | | - Andrew J. Peters
- Bermuda Institute of Ocean Sciences, 17 Biological Station, St. George’s, GE01, Bermuda
| | - Amy Jo Scarino
- NASA Langley Research Center, Hampton, VA, USA
- Science Systems and Applications, Inc., Hampton, VA, USA
| | | | | | - Christiane Voigt
- Institute for Atmospheric Physics, DLR, German Aerospace Center, Oberpfaffenhofen, Germany
- Institute for Atmospheric Physics, University of Mainz, Mainz, Germany
| | - Hailong Wang
- Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Edward Winstead
- NASA Langley Research Center, Hampton, VA, USA
- Science Systems and Applications, Inc., Hampton, VA, USA
| | - Bo Zhang
- National Institute of Aerospace, Hampton, VA, USA
| | - Luke Ziemba
- NASA Langley Research Center, Hampton, VA, USA
| | - Armin Sorooshian
- Department of Chemical and Environmental Engineering, University of Arizona, Tucson, AZ, USA
- Department of Hydrology and Atmospheric Sciences, University of Arizona, Tucson, AZ, USA
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9
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Keller CA, Knowland KE, Duncan BN, Liu J, Anderson DC, Das S, Lucchesi RA, Lundgren EW, Nicely JM, Nielsen E, Ott LE, Saunders E, Strode SA, Wales PA, Jacob DJ, Pawson S. Description of the NASA GEOS Composition Forecast Modeling System GEOS-CF v1.0. JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS 2021; 13:e2020MS002413. [PMID: 34221240 PMCID: PMC8244029 DOI: 10.1029/2020ms002413] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Revised: 02/18/2021] [Accepted: 03/16/2021] [Indexed: 05/11/2023]
Abstract
The Goddard Earth Observing System composition forecast (GEOS-CF) system is a high-resolution (0.25°) global constituent prediction system from NASA's Global Modeling and Assimilation Office (GMAO). GEOS-CF offers a new tool for atmospheric chemistry research, with the goal to supplement NASA's broad range of space-based and in-situ observations. GEOS-CF expands on the GEOS weather and aerosol modeling system by introducing the GEOS-Chem chemistry module to provide hindcasts and 5-days forecasts of atmospheric constituents including ozone (O3), carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2), and fine particulate matter (PM2.5). The chemistry module integrated in GEOS-CF is identical to the offline GEOS-Chem model and readily benefits from the innovations provided by the GEOS-Chem community. Evaluation of GEOS-CF against satellite, ozonesonde and surface observations for years 2018-2019 show realistic simulated concentrations of O3, NO2, and CO, with normalized mean biases of -0.1 to 0.3, normalized root mean square errors between 0.1-0.4, and correlations between 0.3-0.8. Comparisons against surface observations highlight the successful representation of air pollutants in many regions of the world and during all seasons, yet also highlight current limitations, such as a global high bias in SO2 and an overprediction of summertime O3 over the Southeast United States. GEOS-CF v1.0 generally overestimates aerosols by 20%-50% due to known issues in GEOS-Chem v12.0.1 that have been addressed in later versions. The 5-days forecasts have skill scores comparable to the 1-day hindcast. Model skills can be improved significantly by applying a bias-correction to the surface model output using a machine-learning approach.
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Affiliation(s)
- Christoph A. Keller
- NASA Goddard Space Flight CenterGreenbeltMDUSA
- Universities Space Research AssociationColumbiaMDUSA
| | - K. Emma Knowland
- NASA Goddard Space Flight CenterGreenbeltMDUSA
- Universities Space Research AssociationColumbiaMDUSA
| | | | - Junhua Liu
- NASA Goddard Space Flight CenterGreenbeltMDUSA
- Universities Space Research AssociationColumbiaMDUSA
| | - Daniel C. Anderson
- NASA Goddard Space Flight CenterGreenbeltMDUSA
- Universities Space Research AssociationColumbiaMDUSA
| | - Sampa Das
- NASA Goddard Space Flight CenterGreenbeltMDUSA
- Universities Space Research AssociationColumbiaMDUSA
| | - Robert A. Lucchesi
- NASA Goddard Space Flight CenterGreenbeltMDUSA
- Science Systems and Applications, Inc.LanhamMDUSA
| | | | - Julie M. Nicely
- NASA Goddard Space Flight CenterGreenbeltMDUSA
- Earth System Science Interdisciplinary CenterUniversity of MarylandCollege ParkLanhamMDUSA
| | - Eric Nielsen
- NASA Goddard Space Flight CenterGreenbeltMDUSA
- Science Systems and Applications, Inc.LanhamMDUSA
| | | | - Emily Saunders
- NASA Goddard Space Flight CenterGreenbeltMDUSA
- Science Systems and Applications, Inc.LanhamMDUSA
| | - Sarah A. Strode
- NASA Goddard Space Flight CenterGreenbeltMDUSA
- Universities Space Research AssociationColumbiaMDUSA
| | - Pamela A. Wales
- NASA Goddard Space Flight CenterGreenbeltMDUSA
- Universities Space Research AssociationColumbiaMDUSA
| | - Daniel J. Jacob
- School of Engineering and Applied SciencesHarvard UniversityCambridgeMAUSA
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10
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Lin J, Du M, Chen L, Feng K, Liu Y, V Martin R, Wang J, Ni R, Zhao Y, Kong H, Weng H, Liu M, van Donkelaar A, Liu Q, Hubacek K. Carbon and health implications of trade restrictions. Nat Commun 2019; 10:4947. [PMID: 31666528 PMCID: PMC6821914 DOI: 10.1038/s41467-019-12890-3] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2019] [Accepted: 09/30/2019] [Indexed: 12/03/2022] Open
Abstract
In a globalized economy, production of goods can be disrupted by trade disputes. Yet the resulting impacts on carbon dioxide emissions and ambient particulate matter (PM2.5) related premature mortality are unclear. Here we show that in contrast to a free trade world, with the emission intensity in each sector unchanged, an extremely anti-trade scenario with current tariffs plus an additional 25% tariff on each traded product would reduce the global export volume by 32.5%, gross domestic product by 9.0%, carbon dioxide by 6.3%, and PM2.5-related mortality by 4.1%. The respective impacts would be substantial for the United States, Western Europe and China. A freer trade scenario would increase global carbon dioxide emission and air pollution due to higher levels of production, especially in developing regions with relatively high emission intensities. Global collaborative actions to reduce emission intensities in developing regions could help achieve an economic-environmental win-win state through globalization.
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Affiliation(s)
- Jintai Lin
- Laboratory for Climate and Ocean-Atmosphere Studies, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, 100871, China.
| | - Mingxi Du
- Laboratory for Climate and Ocean-Atmosphere Studies, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, 100871, China
| | - Lulu Chen
- Laboratory for Climate and Ocean-Atmosphere Studies, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, 100871, China
| | - Kuishuang Feng
- Institute of Blue and Green Development, Shandong University, Weihai, 264209, China.
- Department of Geographical Sciences, University of Maryland, College Park, MD, 20742, USA.
| | - Yu Liu
- Institutes of Science and Development, Chinese Academy of Sciences, Beijing, 100190, China.
- School of Public Policy and Management, University of Chinese Academy of Sciences, Beijing, 100049, China.
| | - Randall V Martin
- Department of Energy, Environmental & Chemical Engineering, Washington University in St. Louis, St. Louis, Missouri, United States
- Department of Physics and Atmospheric Science, Dalhousie University, Halifax, NS, B3H 4R2, Canada
- Smithsonian Astrophysical Observatory, Harvard-Smithsonian Center for Astrophysics, Cambridge, MA, 02138, USA
| | - Jingxu Wang
- Laboratory for Climate and Ocean-Atmosphere Studies, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, 100871, China
| | - Ruijing Ni
- Laboratory for Climate and Ocean-Atmosphere Studies, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, 100871, China
| | - Yu Zhao
- School of the Environment, Nanjing University, 163 Xianlin Ave, Nanjing, 210046, China
| | - Hao Kong
- Laboratory for Climate and Ocean-Atmosphere Studies, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, 100871, China
| | - Hongjian Weng
- Laboratory for Climate and Ocean-Atmosphere Studies, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, 100871, China
| | - Mengyao Liu
- Laboratory for Climate and Ocean-Atmosphere Studies, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, 100871, China
| | - Aaron van Donkelaar
- Department of Energy, Environmental & Chemical Engineering, Washington University in St. Louis, St. Louis, Missouri, United States
- Department of Physics and Atmospheric Science, Dalhousie University, Halifax, NS, B3H 4R2, Canada
| | - Qiuyu Liu
- Department of Biological Sciences, University of Quebec at Montreal, Montreal, H3C 3P8, Canada
| | - Klaus Hubacek
- Energy and Sustainability Research Institute Groningen (ESRIG), University of Groningen, Nijenborg 6, 9747 AG, Groningen, The Netherlands
- International Institute for Applied Systems Analysis, Schlossplatz 1, A-2361, Laxenburg, Austria
- Department of Environmental Studies, Masaryk University, Jostova 10, 602 00, Brno, Czech Republic
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11
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Chen X, Millet DB, Singh HB, Wisthaler A, Apel EC, Atlas EL, Blake DR, Bourgeois I, Brown SS, Crounse JD, de Gouw JA, Flocke FM, Fried A, Heikes BG, Hornbrook RS, Mikoviny T, Min KE, Müller M, Neuman JA, O'Sullivan DW, Peischl J, Pfister GG, Richter D, Roberts JM, Ryerson TB, Shertz SR, Thompson CR, Treadaway V, Veres PR, Walega J, Warneke C, Washenfelder RA, Weibring P, Yuan B. On the sources and sinks of atmospheric VOCs: an integrated analysis of recent aircraft campaigns over North America. ATMOSPHERIC CHEMISTRY AND PHYSICS 2019; 19:9097-9123. [PMID: 33688334 PMCID: PMC7939023 DOI: 10.5194/acp-19-9097-2019] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
We apply a high-resolution chemical transport model (GEOS-Chem CTM) with updated treatment of volatile organic compounds (VOCs) and a comprehensive suite of airborne datasets over North America to (i) characterize the VOC budget and (ii) test the ability of current models to capture the distribution and reactivity of atmospheric VOCs over this region. Biogenic emissions dominate the North American VOC budget in the model, accounting for 70 % and 95 % of annually emitted VOC carbon and reactivity, respectively. Based on current inventories anthropogenic emissions have declined to the point where biogenic emissions are the dominant summertime source of VOC reactivity even in most major North American cities. Methane oxidation is a 2x larger source of nonmethane VOCs (via production of formaldehyde and methyl hydroperoxide) over North America in the model than are anthropogenic emissions. However, anthropogenic VOCs account for over half of the ambient VOC loading over the majority of the region owing to their longer aggregate lifetime. Fires can be a significant VOC source episodically but are small on average. In the planetary boundary layer (PBL), the model exhibits skill in capturing observed variability in total VOC abundance (R 2 = 0:36) and reactivity (R 2 = 0:54). The same is not true in the free troposphere (FT), where skill is low and there is a persistent low model bias (~ 60 %), with most (27 of 34) model VOCs underestimated by more than a factor of 2. A comparison of PBL: FT concentration ratios over the southeastern US points to a misrepresentation of PBL ventilation as a contributor to these model FT biases. We also find that a relatively small number of VOCs (acetone, methanol, ethane, acetaldehyde, formaldehyde, isoprene C oxidation products, methyl hydroperoxide) drive a large fraction of total ambient VOC reactivity and associated model biases; research to improve understanding of their budgets is thus warranted. A source tracer analysis suggests a current overestimate of biogenic sources for hydroxyacetone, methyl ethyl ketone and glyoxal, an underestimate of biogenic formic acid sources, and an underestimate of peroxyacetic acid production across biogenic and anthropogenic precursors. Future work to improve model representations of vertical transport and to address the VOC biases discussed are needed to advance predictions of ozone and SOA formation.
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Affiliation(s)
- Xin Chen
- Department of Soil, Water, and Climate, University of Minnesota, Minneapolis-Saint Paul, MN, USA
| | - Dylan B. Millet
- Department of Soil, Water, and Climate, University of Minnesota, Minneapolis-Saint Paul, MN, USA
| | | | - Armin Wisthaler
- Institute for Ion Physics and Applied Physics, University of Innsbruck, 6020 Innsbruck, Austria
- Department of Chemistry, University of Oslo, Oslo, Norway
| | - Eric C. Apel
- Atmospheric Chemistry Observations & Modeling Laboratory, National Center for Atmospheric Research, Boulder, CO, USA
| | - Elliot L. Atlas
- Department of Atmospheric Sciences, Rosenstiel School of Marine and Atmospheric Science, University of Miami, Miami, FL, USA
| | - Donald R. Blake
- Department of Chemistry, University of California, Irvine, Irvine, CA, USA
| | - Ilann Bourgeois
- Chemical Sciences Division, NOAA Earth System Research Laboratory, Boulder, CO, USA
- Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, CO, USA
| | - Steven S. Brown
- Chemical Sciences Division, NOAA Earth System Research Laboratory, Boulder, CO, USA
| | - John D. Crounse
- Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, CA, USA
| | - Joost A. de Gouw
- Chemical Sciences Division, NOAA Earth System Research Laboratory, Boulder, CO, USA
- Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, CO, USA
| | - Frank M. Flocke
- Atmospheric Chemistry Observations & Modeling Laboratory, National Center for Atmospheric Research, Boulder, CO, USA
| | - Alan Fried
- Institute of Arctic & Alpine Research, University of Colorado, Boulder, CO, USA
| | - Brian G. Heikes
- Graduate School of Oceanography, University of Rhode Island, Narragansett, RI, USA
| | - Rebecca S. Hornbrook
- Atmospheric Chemistry Observations & Modeling Laboratory, National Center for Atmospheric Research, Boulder, CO, USA
| | - Tomas Mikoviny
- Department of Chemistry, University of Oslo, Oslo, Norway
| | - Kyung-Eun Min
- School of Earth Science and Environmental Engineering, Gwangju Institute of Science and Technology, Gwangju, South Korea
| | - Markus Müller
- Institute for Ion Physics and Applied Physics, University of Innsbruck, 6020 Innsbruck, Austria
| | - J. Andrew Neuman
- Chemical Sciences Division, NOAA Earth System Research Laboratory, Boulder, CO, USA
- Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, CO, USA
| | | | - Jeff Peischl
- Chemical Sciences Division, NOAA Earth System Research Laboratory, Boulder, CO, USA
- Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, CO, USA
| | - Gabriele G. Pfister
- Atmospheric Chemistry Observations & Modeling Laboratory, National Center for Atmospheric Research, Boulder, CO, USA
| | - Dirk Richter
- Institute of Arctic & Alpine Research, University of Colorado, Boulder, CO, USA
| | - James M. Roberts
- Chemical Sciences Division, NOAA Earth System Research Laboratory, Boulder, CO, USA
| | - Thomas B. Ryerson
- Chemical Sciences Division, NOAA Earth System Research Laboratory, Boulder, CO, USA
| | - Stephen R. Shertz
- Atmospheric Chemistry Observations & Modeling Laboratory, National Center for Atmospheric Research, Boulder, CO, USA
| | - Chelsea R. Thompson
- Chemical Sciences Division, NOAA Earth System Research Laboratory, Boulder, CO, USA
- Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, CO, USA
| | - Victoria Treadaway
- Graduate School of Oceanography, University of Rhode Island, Narragansett, RI, USA
| | - Patrick R. Veres
- Chemical Sciences Division, NOAA Earth System Research Laboratory, Boulder, CO, USA
| | - James Walega
- Institute of Arctic & Alpine Research, University of Colorado, Boulder, CO, USA
| | - Carsten Warneke
- Chemical Sciences Division, NOAA Earth System Research Laboratory, Boulder, CO, USA
- Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, CO, USA
| | | | - Petter Weibring
- Institute of Arctic & Alpine Research, University of Colorado, Boulder, CO, USA
| | - Bin Yuan
- Institute for Environmental and Climate Research, Jinan University, Guangzhou, China
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12
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Schuh AE, Jacobson AR, Basu S, Weir B, Baker D, Bowman K, Chevallier F, Crowell S, Davis KJ, Deng F, Denning S, Feng L, Jones D, Liu J, Palmer PI. Quantifying the Impact of Atmospheric Transport Uncertainty on CO 2 Surface Flux Estimates. GLOBAL BIOGEOCHEMICAL CYCLES 2019; 33:484-500. [PMID: 31244506 PMCID: PMC6582606 DOI: 10.1029/2018gb006086] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/18/2018] [Revised: 02/11/2019] [Accepted: 03/09/2019] [Indexed: 05/15/2023]
Abstract
We show that transport differences between two commonly used global chemical transport models, GEOS-Chem and TM5, lead to systematic space-time differences in modeled distributions of carbon dioxide and sulfur hexafluoride. The distribution of differences suggests inconsistencies between the transport simulated by the models, most likely due to the representation of vertical motion. We further demonstrate that these transport differences result in systematic differences in surface CO2 flux estimated by a collection of global atmospheric inverse models using TM5 and GEOS-Chem and constrained by in situ and satellite observations. While the impact on inferred surface fluxes is most easily illustrated in the magnitude of the seasonal cycle of surface CO2 exchange, it is the annual carbon budgets that are particularly relevant for carbon cycle science and policy. We show that inverse model flux estimates for large zonal bands can have systematic biases of up to 1.7 PgC/year due to large-scale transport uncertainty. These uncertainties will propagate directly into analysis of the annual meridional CO2 flux gradient between the tropics and northern midlatitudes, a key metric for understanding the location, and more importantly the processes, responsible for the annual global carbon sink. The research suggests that variability among transport models remains the largest source of uncertainty across global flux inversion systems and highlights the importance both of using model ensembles and of using independent constraints to evaluate simulated transport.
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Affiliation(s)
- Andrew E. Schuh
- Cooperative Institute for Research in the AtmosphereColorado State UniversityFort CollinsCOUSA
| | - Andrew R. Jacobson
- University of Colorado Boulder and NOAA Earth System Research LaboratoryBoulderCOUSA
| | - Sourish Basu
- University of Colorado Boulder and NOAA Earth System Research LaboratoryBoulderCOUSA
| | - Brad Weir
- Global Modeling and Assimilation OfficeNASA Goddard Space Flight CenterGreenbeltMDUSA
| | - David Baker
- Cooperative Institute for Research in the AtmosphereColorado State UniversityFort CollinsCOUSA
| | - Kevin Bowman
- Jet Propulsion LaboratoryCalifornia Institute of TechnologyPasadenaCAUSA
| | - Frédéric Chevallier
- Laboratoire des Sciences du Climat et de l'Environnement, CEA‐CNRS‐UVSQ, L'Orme des Merisiers, GifsurYvetteParisFrance
| | - Sean Crowell
- School of MeteorologyUniversity of OklahomaNormanOKUSA
| | - Kenneth J. Davis
- Department of Meteorology and Atmospheric SciencePennsylvania State UniversityUniversity ParkPAUSA
| | - Feng Deng
- Department of PhysicsUniversity of TorontoTorontoOntarioCanada
| | - Scott Denning
- Department of Atmospheric SciencesColorado State UniversityFort CollinsCOUSA
| | - Liang Feng
- School of GeoSciencesUniversity of EdinburghEdinburghUK
- National Centre for Earth ObservationEdinburghUK
| | - Dylan Jones
- Department of PhysicsUniversity of TorontoTorontoOntarioCanada
| | - Junjie Liu
- Jet Propulsion LaboratoryCalifornia Institute of TechnologyPasadenaCAUSA
| | - Paul I. Palmer
- School of GeoSciencesUniversity of EdinburghEdinburghUK
- National Centre for Earth ObservationEdinburghUK
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13
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Giang A, Song S, Muntean M, Janssens-Maenhout G, Harvey A, Berg E, Selin NE. Understanding factors influencing the detection of mercury policies in modelled Laurentian Great Lakes wet deposition. ENVIRONMENTAL SCIENCE. PROCESSES & IMPACTS 2018; 20:1373-1389. [PMID: 30247491 DOI: 10.1039/c8em00268a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
We used chemical transport modelling to better understand the extent to which policy-related anthropogenic mercury emissions changes (a policy signal) can be statistically detected in wet deposition measurements in the Great Lakes region on the subdecadal scale, given sources of noise. In our modelling experiment, we consider hypothetical regional (North American) and global (rest of the world) policy changes, consistent with existing policy efforts (Δglobal = -18%; Δregional = -30%) that divide an eight-year period. The magnitude of statistically significant (p < 0.1) pre- and post-policy period wet deposition differences, holding all else constant except for the policy change, ranges from -0.3 to -2.0% for the regional policy and -0.8 to -2.7% for the global policy. We then introduce sources of noise-trends and variability in factors that are exogenous to the policy action-and evaluate the extent to which the policy signals can still be detected. For instance, technology-related variability in emissions magnitude and speciation can shift the magnitude of differences between periods, in some cases dampening the policy effect. We have found that the interannual variability in meteorology has the largest effect of the sources of noise considered, driving deposition differences between periods to ±20%, exceeding the magnitude of the policy signal. However, our simulations suggest that gaseous elemental mercury concentration may be more robust to this meteorological variability in this region, and a stronger indicator of local/regional emissions changes. These results highlight the potential challenges of detecting statistically significant policy-related changes in Great Lakes wet deposition within the subdecadal scale.
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Affiliation(s)
- Amanda Giang
- Institute for Data, Systems and Society, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
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